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Simulation and Evaluation of Cloud Storage Caching for Data Intensive Science.

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Commercial cloud storage can act as a flexible cache, reducing on-premises disk needs for data reduction workflows. This approach maintains job throughput for continuously processed data, optimizing storage performance.

Keywords:
Cloud storageQuality-of-service storageTransfer simulation

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Area of Science:

  • Scientific computing
  • Data management
  • Cloud computing

Background:

  • Data reduction workflows are crucial in scientific computing for extracting key information from large datasets.
  • Traditional storage setups use tape for archival and disk for frequent access, but unpredictable data access patterns can degrade tape performance.
  • Storing copies of large input data on disk is a common but potentially costly solution.

Purpose of the Study:

  • To evaluate the use of commercial cloud storage as a flexible cache or buffer for data reduction workflows.
  • To analyze the performance of cloud storage in scenarios with continuously processed and unpredictably accessed data.
  • To develop and utilize a simulation tool for analyzing storage and network resource models.

Main Methods:

  • Developed a simulation tool to model and analyze storage and network resource performance.
  • Evaluated a model using commercial cloud storage as a flexible cache for data reduction.
  • Focused the evaluation on workflows with continuously processed data.

Main Results:

  • Commercial cloud storage can decrease the need for on-premises disk storage.
  • The proposed cloud caching model maintains job throughput equivalent to traditional methods.
  • Simulation analysis provides insights into key performance metrics and decision-making for cloud storage adoption.

Conclusions:

  • Commercial cloud storage offers a viable solution for optimizing data reduction workflows with unpredictable access patterns.
  • The simulation tool aids in evaluating the cost-effectiveness and performance benefits of cloud storage integration.
  • This approach addresses future data challenges by providing flexible and efficient data management strategies.